Multi-Channel Stimulation Threshold Detection Algorithm For Use In Neurophysiology Monitoring

ABSTRACT

The present invention relates generally to an algorithm aimed at neurophysiology monitoring, and more particularly to an algorithm capable of quickly finding stimulation thresholds over multiple channels of a neurophysiology monitoring system.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is an international patent application claiming thebenefit of priority from commonly owned and co-pending U.S. ProvisionalPatent Application Ser. No. 60/719,897, entitled “Multi-ChannelStimulation Threshold Detection Algorithm for Use With NeurophysiologyMonitoring Systems,” and filed on Sep. 22, 2005.

BACKGROUND OF THE INVENTION

1. Field

The present invention relates generally to an algorithm aimed atneurophysiology monitoring, and more particularly to an algorithmcapable of quickly finding stimulation thresholds over multiple channelsof a neurophysiology monitoring system.

2. Background

The risk of neurological impairment is a prime concern when performingsurgical procedures in close proximity to the spine or nerves. To combatthis risk, surgeons are increasingly relying on neurophysiologymonitoring techniques to monitor nerves and alert them to potentialimpairment during a surgical procedure. Often times effective nervemonitoring requires monitoring neurophysiologic results over a multitudeof channels. While this is generally advantageous, it may have thenegative effect of increasing the time required to complete nervemonitoring and therefore increasing the overall surgery time as well,which in turn increases the costs and risks associated with the surgery.Based on the foregoing, a need exists for an improved means ofneurophysiology monitoring, and in particular a needs exists for a meansto reduce the time required to monitor neurophysiologic results over amultitude of channels. The present invention is aimed at addressingthese needs.

SUMMARY OF THE INVENTION

The present invention endows surgeons with valuable information thatallows for the efficient assessment of risk to neural tissue before,during, and/or after a surgical procedure. This is accomplished byquickly and accurately determining a stimulation threshold for neuraltissue and relaying that information to the surgeon in a simplecomprehensible fashion. Stimulation thresholds are determined byelectrically stimulating nerve tissue and analyzing resulting muscleactivity relative to determine the stimulation current level at whichnerve tissue depolarizes. To make stimulation threshold determinations,muscle activity may be monitored by measuring electrical signalsassociated with muscle contraction, called electromyography (“EMG”). EMGresponses can be characterized by a peak-to-peak voltage ofV_(pp)=V_(max)−V_(min). Characteristics of the electrical stimulationsignal used may vary depending upon several factors, including theparticular nerve assessment performed, the spinal target level, the typeof neural tissue stimulated (e.g. nerve root, spinal cord, brain, etc. .. ) among others.

A basic premise underlying the stimulation threshold technique is thatnerves have a characteristic threshold current level (I_(thresh)) atwhich they will depolarize and cause a significant EMG response. Asignificant EMG response may be defined as having a V_(pp) greater thana predetermined threshold voltage (V_(thresh)). By way of example only,the V_(thresh) may be selected from a range including 20 V-100 uV.Stimulation with a current below the threshold level, I_(thresh), willnot evoke a significant EMG response, while stimulation with a currentat or above the threshold level will evoke a significant EMG response.This relationship between the stimulation current and the EMG responsemay be represented via a “recruitment curve.” When stimulation does notevoke a significant EMG response (represented in the onset region) thestimulation current is said to have not “recruited.” When stimulationdoes evoke a significant EMG response (represented in the linear andsaturation regions) the stimulation current is said to have “recruited.”I_(thresh), is the lowest stimulation current that recruits (evokes asignificant EMG response).

The algorithm described herein may considerably reduce the number ofstimulations, and thus time, required to determine I_(thresh),particularly for a number of channels, over the course of a procedure.The basic method for finding I_(thresh) utilizes a bracketing method anda bisection method. The bracketing method quickly finds a range(bracket) of stimulation currents that must contain I_(thresh) and thebisection method narrows the bracket until I_(thresh) is known within aspecified accuracy.

The bracketing method adjusts the stimulation current as follows.Stimulation begins at a minimum stimulation current. Each subsequentstimulation is delivered at a current level double that of the precedingcurrent. This doubling continues until a stimulation current results inan EMG response with a V_(pp) greater than V_(thresh). This firststimulation current to recruit, together with the last stimulationcurrent to have not recruited, forms the initial bracket.

After bracketing the threshold current I_(thresh), the bisection methodis used to reduce the bracket to a selected width or resolution. Thestimulation current at the midpoint of the bracket is used. If thestimulation current recruits, the bracket shrinks to the lower half ofthe previous range. If the stimulation current does not recruit, thebracket shrinks to the upper half of the previous range. This processcontinues until I_(thresh) is bracketed by stimulation currentsseparated by the selected width or resolution. I_(thresh) is preferablydefined as the midpoint of this final bracket. The bracketing andbisection steps may be repeated and I_(thresh) found for each channelunless the threshold exceeds a predetermined maximum current.

To reduce the number of stimulations required to complete the bracketingand bisection steps when I_(thresh) is determined repeatedly and/or overmultiple channels, the algorithm omits stimulations for which the resultis predictable from data acquired during previous stimulations. When astimulation is omitted, the algorithm proceeds as if the stimulation hadtaken place. However, instead of reporting an actual recruitment result,the reported result is inferred from the previous data. This permits thealgorithm to proceed to the next step immediately, without the delayassociated with a stimulation. For every stimulation signal delivered,the EMG response, or lack thereof, is detected and recorded on eachchannel (no matter which channel is actually being processed forI_(thresh)). Later the data can be referred back to, allowing thealgorithm to omit a stimulation and infer whether or not the channelwould recruit at the given stimulation current.

There are two scenarios in which the algorithm may omit a stimulationand report previously obtained recruitment results. A stimulation may beomitted if the selected stimulation current would be a repeat of aprevious stimulation. If the specific stimulation current is not arepeat, the stimulation may be omitted if the results are already clearfrom the previous data.

To determine whether to deliver an actual stimulation or omit thestimulation and report previous results, the algorithm first checkswhether the selected stimulation current has been previously used. Ifthe stimulation current has been used, the stimulation is omitted andthe results of the previous stimulation are reported for the presentchannel. If the stimulation current has not been used, the algorithmdetermines I_(recruit) and I_(norecruit) for the present channel.I_(recruit) is the lowest stimulation current that has recruited on thepresent channel. I_(norecruit) is the highest stimulation current thathas failed to recruit on the present channel. If I_(recruit) is notgreater than I_(norecruit), the algorithm will stimulate at the selectedcurrent and report the results for the present channel. If I_(recruit)is greater than I_(norecruit), the algorithm identifies whether theselected stimulation current is higher than I_(recruit), lower thanI_(norecruit), or between I_(recruit) and I_(norecruit). If the selectedstimulation current is higher than I_(recruit), the algorithm omits thestimulation and reports that the present channel recruits at thespecified current. Conversely, when the selected stimulation current islower than I_(norecruit), the algorithm infers that the present channelwill not recruit at the selected current and reports that result. If theselected stimulation current falls between I_(recruit) andI_(norecruit), the result of the stimulation cannot be inferred. Thealgorithm stimulates at the selected current and reports the results forthe present channel. This method may be repeated until I_(thresh) hasbeen determined for every active channel.

The order in which channels are processed is immaterial. The channelprocessing order may be biased to yield the highest or lowest thresholdfirst or an arbitrary processing order may be used. It is also notnecessary to complete the algorithm for one channel before beginning toprocess the next channel. Channels are still processed one at a time,however, the algorithm may cycle between one or more channels,processing as few as one stimulation current for that channel beforemoving on to the next channel. In this manner the algorithm may advanceall channels essentially together and bias the order to find the lowerthreshold channels first or the higher threshold channels first.

To further reduce the number of stimulations required to repeatedly findI_(thresh) over the course of a procedure, the algorithm includes aconfirmation step. If I_(thresh) has been previously determined for aspecific channel, the algorithm may simply confirm that I_(thresh) hasnot changed rather than beginning anew with the bracketing and bisectionmethods. The algorithm first determines whether it is conducting theinitial threshold determination for the channel or whether there is aprevious I_(thresh) determination. If it is not the initialdetermination, the algorithm confirms the previous determination. If theprevious threshold is confirmed, the algorithm reports that value as thepresent I_(thresh). If it is the initial I_(thresh) determination or ifthe previous threshold cannot be confirmed, the algorithm enters thebracketing and bisection states to determine I_(thresh) and then reportsthe value.

The confirmation step attempts to ascertain whether I_(thresh) has movedfrom its last known value. To do this, the algorithm applies twostimulation currents, one at or just above the threshold value and onejust below the threshold value. If the stimulation at or aboveI_(thresh) recruits and the stimulation just below I_(thresh) does notrecruit, then I_(thresh) is confirmed and the algorithm may report theinitial value again as I_(thresh) and proceed to process anotherchannel. If the stimulation just below I_(thresh) recruits, it may beconcluded that I_(thresh) has decreased and likewise, if the stimulationat or just above I_(thresh) fails to recruit, it may be assumed thatI_(thresh) has increased and therefore I_(thresh) cannot be confirmed.

If I_(thresh) cannot be confirmed, the algorithm enters the bracketingstate. Rather than beginning the bracketing state from the minimumstimulation current, however, the bracketing state may begin from theprevious I_(thresh). The bracketing may advance up or down depending onwhether I_(thresh) has increased or decreased. When the algorithm entersthe bracketing state, the increment used in the confirmation step isexponentially doubled until the channel recruits, at which time itenters the bisection state. The confirmation step may be performed foreach channel, in turn, in any order. Again stimulations may be omittedand the algorithm may begin processing a new channel before completingthe algorithm for another channel, as described above.

The algorithm described herein may be particularly useful when employedto monitor nerve pathology in conjunction with the use of a nerveretractor. A typical nerve retractor serves to pull or otherwisemaintain a nerve outside the surgical corridor, thereby protecting thenerve from inadvertent damage or contact by the “active” instrumentationused to perform the actual surgery. While generally advantageous, it hasbeen observed that such retraction can cause nerve function to becomeimpaired or otherwise pathologic over time due to the retraction.Monitoring I_(thresh) during nerve retraction may be useful to assessthe degree to which retraction of a nerve or neural structure affectsthe nerve function over time. One advantage of such monitoring is thatthe conduction of the nerve may be monitored during the procedure todetermine whether the neurophysiology and/or function of the nervechanges (for the better or worse) as a result of the particular surgicalprocedure. For example, it may be observed that the nerve conductiondecreases (indicated by an increase in I_(thresh) over time) during theretraction, indicating that the nerve function has been negativelyaffected. In contrast, the nerve conduction may increase (indicated by adecrease in I_(thresh) over time), indicating that the nerve functionmay have been restored or improved by the surgical procedure (such asduring a successful decompression surgery, etc . . . ). As mentioned, achange in I_(thresh) may occur on any channel; therefore it isadvantageous to calculate the actual I_(thresh) for each channel, asopposed to determining a value for just the channel with the highest orlowest I_(thresh). The algorithm of the present invention accomplishesthis while substantially limiting the number of stimulations required todo so. This may substantially reduce the time required to make anI_(thresh) determination which in turn may reduce the overall surgicaltime and risk to the patient.

The algorithm of the present invention may also be of particular useduring Motor Evoked Potential (MEP) monitoring. When surgical proceduresare performed in the proximity of the spinal cord, potential damage tothe spinal cord is a paramount concern. Consequences of spinal corddamage may range from a slight loss of sensation to complete paralysisof the extremities, depending on the location and extent of damage. MEPmonitoring, which generally involves monitoring the transmission of anelectrical signal along the spinal cord, may be employed to assess thespinal cord before, during, and/or after surgery. Degradation ordecreased conduction of an electrical signal, indicated by an increasein I_(thresh), may indicate that the health of the spinal cord iscompromised. Obtaining such information quickly may allow the surgeon toinitiate corrective measures before the damage gets worse and/or becomespermanent. Similar to the nerve pathology monitoring mentioned above,changes in I_(thresh) indicating potential damage to the spinal cord mayoccur on any monitored channel, thus it is advantageous to calculate theactual I_(thresh) for each channel, as opposed to determining just thechannel with the highest or lowest I_(thresh). Employing the algorithmof the present invention again allows this to be done accurately andefficiently.

The algorithm of the present invention may be employed for use on any ofa number of neurophysiology monitoring systems. By way of example only,a preferred multi-channel neurophysiology monitoring system foremploying the algorithm of the present invention to quickly findstimulation thresholds for a multitude of channels may be capable ofcarrying out neurophysiologic assessment functions including, but notnecessarily limited to, Twitch Test (neuromuscular pathway assessment),Screw Test (pedicle integrity testing), Detection (nerve proximitytesting during surgical access), Nerve Retractor (nerve pathologymonitoring), MEP (Motor Evoked Potential spinal cord monitoring), andSSEP (Somatosensory Evoked Potential spinal cord monitoring).

The surgical system includes a control unit, a patient module, an MEPstimulator, an EMG harness, including eight pairs of EMG electrodes anda return (anode) electrode coupled to the patient module, at least onepair of stimulation electrodes coupled to the MEP stimulator, and a hostof surgical accessories (including but not limited to a nerve retractor,screw test probe, dynamic stimulation clips, a K-wire, one or moredilating cannula, and a tissue retraction assembly) capable of beingcoupled to the patient module via one or more accessory cables.Information generated by the system is shown on a screen display and mayinclude, but is not necessarily limited to, alpha-numeric and/orgraphical information regarding MEP, nerve pathology, myotome/EMGlevels, stimulation levels, the function selected.

Neural pathology monitoring may be performed by electrically stimulatinga nerve root according to the hunting algorithm, via one or morestimulation electrodes at the distal end of the nerve root retractor andmonitoring each channel for corresponding evoked muscle responses.Threshold hunting continues according to the algorithm until I_(thresh)is determined for each channel in range. A pathology assessment is madeby determining a baseline stimulation threshold with direct contactbetween the nerve retractor and the nerve, prior to retraction.Subsequent stimulation thresholds are determined during retraction andthey are compared to the baseline threshold. An increase in I_(thresh)over time is an indication that the nerve function is deteriorating andretraction should be reduced or stopped altogether to prevent permanentdamage. A decrease in I_(thresh) over time may be an indication thatnerve function has been at least partially restored. The display ofI_(thresh) values may be accompanied by a color code making use of thecolors Red, Yellow, and Green to indicate predetermined unsafe,intermediate and safe levels, respectively.

MEP may be performed by electrically stimulating the motor cortex of thebrain with electrical stimulation signals which creates an actionpotential that travels along the spinal cord and into the descendingnerves, evoking activity from muscles innervated by the nerves. EMGresponses of the muscles are recorded by the system and analyzed inrelation to the stimulation signal. The multi-channel threshold huntingalgorithm described above may be utilized to determine a baselineI_(thresh) for each channel. Having determined a baseline I_(thresh) foreach channel, subsequent monitoring may be performed as desiredthroughout the procedure and recovery period to obtain updatedI_(thresh) values for each channel. Each new determination of I_(thresh)is compared by the surgical system to the baseline I_(thresh) for theappropriate channel. The difference (ΔI_(thresh)) between the baselineI_(thresh) and the new I_(thresh) is calculated and the ΔI_(thresh)value is compared to predetermined “safe” and “unsafe” values. Thedisplay of I_(thresh) may be accompanied by a color code making use ofthe colors Red, Yellow, and Green to indicate predetermined unsafe,intermediate and safe levels, respectively.

BRIEF DESCRIPTION OF THE DRAWINGS

Many advantages of the present invention will be apparent to thoseskilled in the art with a reading of this specification in conjunctionwith the attached drawings, wherein like reference numerals are appliedto like elements and wherein:

FIG. 1 is a graph illustrating a plot of the neuromuscular response(EMG) of a given myotome over time based on a current stimulation pulseapplied to a nerve bundle coupled to the given myotome;

FIG. 2 is a graph illustrating a plot of a stimulation signal capable ofproducing a neuromuscular response (EMG) of the type shown in FIG. 1;

FIG. 3 is a graph illustrating a plot of another embodiment of astimulation signal capable of producing a neuromuscular response (EMG)of the type shown in FIG. 1;

FIG. 4 is a graph illustrating a plot of peak-to-peak voltage (V_(pp))for each given stimulation current level (I_(Stim)) forming astimulation current pulse train according to the present invention(otherwise known as a “recruitment curve”);

FIGS. 5A-4D are graphs illustrating the foundation of a rapidmulti-channel current threshold-hunting algorithm according to oneaspect of the present invention;

FIG. 6 is a flowchart illustrating the method by which the algorithmdetermines whether to perform or omit a stimulation according to oneaspect of the present invention;

FIGS. 7A-7C are graphs illustrating use of the threshold huntingalgorithm of FIG. 5 and further omitting stimulations when the likelyresult is already clear from previous data according to one aspect ofthe present invention;

FIG. 8 is a flowchart illustrating the sequence employed by thealgorithm to determine and monitor I_(thresh) according to one aspect ofthe present invention;

FIG. 9 is a graph illustrating the confirmation step employed by thealgorithm to determine whether I_(thresh) has changed from a previousdetermination according to one aspect of the present invention;

FIG. 10 is a perspective view of an exemplary surgical system 40 capableof employing the algorithm of the present invention to monitorI_(thresh) over a multitude of channels;

FIG. 11 is a block diagram of the surgical system 40 shown in FIG. 10;

FIG. 12 is an exemplary screen display illustrating one embodiment of anerve pathology monitoring function of the surgical system 40 utilizingthe algorithm of the present invention to determine I_(thresh); and

FIG. 13 is an exemplary screen display illustrating one embodiment of atranscranial motor evoked potential monitoring function of the surgicalsystem 40 utilizing the algorithm of the present invention to determineI_(thresh.)

DESCRIPTION OF THE PREFERRED EMBODIMENT

Illustrative embodiments of the invention are described below. In theinterest of clarity, not all features of an actual implementation aredescribed in this specification. It will of course be appreciated thatin the development of any such actual embodiment, numerousimplementation-specific decisions must be made to achieve thedevelopers' specific goals, such as compliance with system-related andbusiness-related constraints, which will vary from one implementation toanother. Moreover, it will be appreciated that such a development effortmight be complex and time-consuming, but would nevertheless be a routineundertaking for those of ordinary skill in the art having the benefit ofthis disclosure. The methods disclosed herein boast a variety ofinventive features and components that warrant patent protection, bothindividually and in combination.

The present invention endows surgeons with valuable information thatallows for the efficient assessment of risk to neural tissue before,during, and/or after a surgical procedure. This is accomplished byquickly and accurately determining a stimulation threshold for neuraltissue and relaying that information to the surgeon in a simplecomprehensible fashion. Stimulation thresholds are determined byelectrically stimulating nerve tissue and analyzing resulting muscleactivity relative to determine the stimulation current level at whichnerve tissue depolarizes. To make stimulation threshold determinations,muscle activity may be monitored by measuring electrical signalsassociated with muscle contraction, called electromyography (“EMG”). EMGresponses, such as that represented in FIG. 1, can be characterized by apeak-to-peak voltage of V_(pp)=V_(max)−V_(min). Characteristics of theelectrical stimulation signal used may vary depending upon severalfactors including, the particular nerve assessment performed, the spinaltarget level, the type of neural tissue stimulated (e.g. nerve root,spinal cord, brain, etc . . . ) among others. By way of example, asingle pulse stimulation signal (such as that illustrated by way ofexample in FIG. 2) or a multi-pulse stimulation signal (such as thatillustrated by way of example in FIG. 3) may be used.

A basic premise underlying the stimulation threshold technique is thatnerves have a characteristic threshold current level (I_(thresh)) atwhich they will depolarize and cause a significant EMG response. Asignificant EMG response may be defined as having a V_(pp) greater thana predetermined threshold voltage (V_(thresh)), such as, by way ofexample only, 100 μV. Stimulation with a current below the thresholdlevel, I_(thresh), will not evoke a significant EMG response, whilestimulation with a current at or above the threshold level will evoke asignificant EMG response. This relationship between the stimulationcurrent and the EMG response may be represented via a “recruitmentcurve,” such as that illustrated in FIG. 4. When stimulation does notevoke a significant EMG response (represented in the onset region), thestimulation current is said to have not “recruited.” When stimulationdoes evoke a significant EMG response (represented in the linear andsaturation regions), the stimulation current is said to have“recruited.” The stimulation threshold, I_(thresh), is the loweststimulation current that recruits (evokes a significant EMG response).

Knowing I_(thresh) allows the surgeon to make various useful assessmentsregarding the safety of nerves during a surgical procedure. For example,it is often necessary to move or maintain a nerve outside of thesurgical area using a nerve retractor. While retraction is generallynecessary to provide better access to the surgical area and protect thenerve from inadvertent damage (e.g. through contact with varioussurgical implements), over time such retraction may impair nerve. Adecrease in nerve function is likely to be accompanied by acorresponding increase in I_(thresh) as a greater stimulation will berequired to depolarize the nerve. Thus, by monitoring for changes inI_(thresh) over the course of retraction, the surgeon may be alerted topotential danger and take steps to correct the condition (e.g. such asreleasing or reducing pressure on the nerve) before nerve impairmentgets worse and/or becomes permanent.

In many cases, to effectively utilize the valuable informationI_(thresh) provides, I_(thresh) must be determined frequently and for anumber of different channels (corresponding to different EMG recordingsites and the muscles they monitor) because I_(thresh) may vary betweenchannels. Additionally, changes in I_(thresh) (indicating a potentialproblem) may occur independently on one channel and not another, therebynecessitating repeated determinations over multiple channels in order togain the maximum benefit. Numerous stimulations may potentially berequired to make a single I_(thresh) determination and making I_(thresh)determinations for multiple channels significantly increases thispotential. For each stimulation signal emitted, a certain period of time(equaling the signal duration plus nerve recovery time) is exhausted.Over a number of stimulations this time adds up, such that the surgeonmay experience a lag time upwards of 30 seconds or longer betweeninitiating a test and receiving the I_(thresh) for each channel. Addedover an entire procedure this may amount to a significant increase insurgery time and/or cause a reluctance to monitor altogether.

The algorithm described herein may considerably reduce the number ofstimulations, and thus time, required to determine I_(thresh). Thisreduction may be especially evident when determining I_(thresh) overevery channel of a multi-channel neurophysiology monitoring system, suchas that described below. FIGS. 5A-5D illustrate the fundamental steps ofa threshold hunting algorithm used to quickly and accurately determineI_(thresh). I_(thresh) is, once again, the minimum stimulation current(I_(stim)) that results in an EMG response with a V_(pp) greater than apredetermined threshold voltage, V_(thresh). The basic method forfinding I_(thresh) utilizes a combination of a bracketing method and abisection method. The bracketing method quickly finds a range (bracket)of stimulation currents that must contain I_(thresh) and the bisectionmethod narrows the bracket until I_(thresh) is known within a specifiedaccuracy. If I_(thresh) on a given channel exceeds a predeterminedmaximum stimulation current, that threshold is considered out of range.

To find the initial bracket, the bracketing method adjusts thestimulation current as follows. Stimulation begins at a predeterminedminimum stimulation current. The minimum stimulation current dependsupon the selected function, by way of example only, the minimumstimulation current used for nerve pathology monitoring may be 1.0 mAwhile the minimum stimulation current used for MEP monitoring may be 60mA. Each subsequent stimulation is delivered at a current level doublethat of the preceding current. This exponential doubling continues untila stimulation current results in an EMG response with a V_(pp) greaterthan V_(thresh) (i.e. it recruits). This first stimulation current torecruit, together with the last stimulation current to have notrecruited, forms the initial bracket, as illustrated in FIG. 5B.

With respect to FIGS. 5C and 5D, after bracketing I_(thresh), thebisection method is used as follows to reduce the bracket to a selectedwidth, shown here by way of example only as 0.1 mA. Bracketing begins bystimulating with a current at the midpoint of the initial bracket. Ifthe stimulation current recruits, the bracket shrinks to the lower halfof the previous range. If the stimulation current does not recruit, thebracket shrinks to the upper half of the previous range. This processcontinues until I_(thresh) is bracketed by stimulation currentsseparated by the selected width or resolution, 0.1 mA in this example.I_(thresh) may be defined as any point falling within the final bracketsuch as for example, the midpoint of the bracket, the upper end of thebracket, and the lower end of the bracket. The bracketing and bisectionsteps may be repeated for all channels until I_(thresh) is determinedfor each one.

Significantly, the algorithm further operates to reduce the number ofactual stimulations required to complete bracketing and bisection whenI_(thresh) is determined repeatedly and/or over multiple channels. Thealgorithm does so by omitting stimulations for which the result ispredictable from data acquired during previous stimulations. When astimulation is omitted, the algorithm proceeds as if the stimulation hadtaken place. Instead of reporting an actual recruitment result, however,the reported result is inferred from the previous data. This permits thealgorithm to proceed to the next step immediately, without the delayassociated with a stimulation.

For every stimulation signal delivered, the EMG response, or lackthereof, is detected and recorded on each channel, no matter whichchannel is actually being processed for I_(thresh). That is, everychannel either recruits or does not recruit (again, a channel is said tohave recruited if a stimulation signal evokes a significant EMG responsefrom the muscle associated with that channel) in response to a givenstimulation signal. These recruitment results are detected and saved foreach channel. Later, when a different channel is processed forI_(thresh), the saved data can be referred back to such that thealgorithm may omit a stimulation if it may infer whether or not thechannel would recruit at the given stimulation current.

There are two scenarios in which the algorithm may omit a stimulationand report previously obtained recruitment results. A stimulation may beomitted if the selected stimulation current would be a repeat of aprevious stimulation. By way of example only, if a stimulation at 1.0 mAwas performed to determine I_(thresh) for one channel, and a stimulationat 1.0 mA is later required to determine I_(thresh) for another channel,the algorithm may omit the stimulation and report the previous results.If the specific stimulation current required has not previously beenused, a stimulation may still be omitted if the results are alreadyclear from the previous data. By way of example only, if a stimulationat 2.0 mA was performed to determine I_(thresh) for a previous channeland the present channel did not recruit, when a stimulation at 1.0 mA islater required to determine I_(thresh) for the present channel, thealgorithm may infer that the present channel will not recruit at 1.0 mAsince it did not recruit at 2.0 mA. The algorithm may omit thestimulation and report the previous result.

FIG. 6 illustrates (in flowchart form) a method by which the algorithmdetermines whether to deliver an actual stimulation or omit thestimulation and report previous results. The algorithm first determinesif the selected stimulation current has been previously used (step 2).If the stimulation current has been used, the stimulation is omitted andthe results of the previous stimulation are reported for the presentchannel (step 4). If the stimulation current has not been used, thealgorithm determines I_(recruit) (step 6) and I_(norecruit) (step 8) forthe present channel. I_(recruit) is the lowest stimulation current thathas recruited on the present channel. I_(norecruit) is the higheststimulation current that has failed to recruit on the present channel.Next the algorithm determines if I_(recruit) is greater thanI_(norecruit) (step 10). An I_(recruit) that is less than or equal toI_(norecruit) is indicative of a changing I_(thresh). Thus, previousresults are not likely reflective of the present threshold state and thealgorithm will not use them to infer a response to a given stimulationcurrent. The algorithm will stimulate at the selected current and reportthe results for the present channel (step 12). If I_(recruit) is greaterthan I_(norecruit), the algorithm next identifies whether the selectedstimulation current is higher than I_(recruit), lower thanI_(norecruit), or between I_(recruit) and I_(norecruit) (step 14). Ifthe selected stimulation current is higher than I_(recruit), thealgorithm omits the stimulation and reports that the present channelrecruits at the specified current (step 16). Conversely, when theselected stimulation current is lower than I_(norecruit), the algorithminfers that the present channel will not recruit at the selected currentand reports that result (step 18). If the selected stimulation currentfalls between I_(recruit) and I_(norecruit), the result of thestimulation cannot be inferred. The algorithm stimulates at the selectedcurrent and reports the results for the present channel (step 12). Thismethod may be repeated until I_(thresh) has been determined for everyactive channel.

For the purposes of example only, FIGS. 7A-7C demonstrate use of thealgorithm of the present invention to determine I_(thresh) on twochannels. It should be appreciated, however, that the algorithm of thepresent invention is not limited to finding I_(thresh) for two channelsbut may be used to find I_(thresh) for any number of channels. It shouldalso be appreciated that the current levels used herein are forexemplary purposes only and the current levels utilized during an actualimplementation may vary considerably from very low currents (e.g. 0.1mA) to very high currents (e.g. 1000 mA), depending upon a number offactors, including, but not necessarily limited to, the function beingperformed and individual patient characteristics, among others. Withreference to FIG. 7A, channel 1 has an I_(thresh) to be found of 12.5 mAand channel 2 has an I_(thresh) to be found of 8.5 mA. I_(thresh) forchannel 1 is found first, using the bracketing and bisection methodsdiscussed above, as illustrated in FIG. 7B. Bracketing begins at theminimum stimulation current (for the purposes of example only) of 1 mA.As this is the first channel processed and no previous recruitmentresults exist, no stimulations are omitted. The stimulation current isdoubled with each successive stimulation (i.e. 1 mA→2 mA→4 mA→8 mA→16mA) until a significant EMG response is finally evoked at 16 mA. Theinitial bracket of 8 mA-16 mA is bisected, using the bisection methoddescribed above (i.e. 12 mA (midpoint of initial bracket)→14 mA(midpoint of bracket 2)→13 mA (midpoint of bracket 3)), until thestimulation threshold is determined to be 12.5 mA, the midpoint of thefinal bracket. Having found I_(thresh) on channel 1, the algorithm mayturn to channel 2, as illustrated in FIG. 7C. The algorithm begins toprocess channel 2 by determining the initial bracket, which is again 8mA-16 mA. In doing so, the algorithm refers back to the data obtainedfor channel 2 during channel 1 processing. All the stimulation currentsrequired in the bracketing state were used in determining I_(thresh) forchannel 1. From the data gathered during channel 1 processing, thealgorithm infers that channel 2 will not recruit at stimulation currentsof 1, 2, 4, and 8 mA and will recruit at 16 mA. These stimulations areomitted and the inferred results are reported in turn.

The first stimulation current selected in the bisection state, 12 mA,was used previously and the algorithm may omit the stimulation andreport that channel 2 recruits at that stimulation current. The nextstimulation current selected in the bisection phase, 10 mA, was notpreviously used and the algorithm must therefore determine whether theresult of a stimulation at 10 mA may still be inferred. I_(recruit) andI_(norecruit) are determined to be 12 mA and 8 mA respectively. 10 mAlies in between the I_(recruit) value of 12 mA and I_(norecruit) valueof 8 mA, thus the result may not be inferred from the previous data andthe stimulation may not be omitted. The algorithm stimulates at 10 mAand reports that the channel recruits. The bracket shrinks to the lowerhalf, making 9 mA the next stimulation current. 9 mA has not previouslybeen used so the algorithm again determines I_(recruit) andI_(norecruit), now 10 mA and 8 mA respectively. The selected stimulationcurrent, 9 mA, falls inbetween I_(recruit) and I_(norecruit), thus, thealgorithm stimulates at 9 mA and reports the results. The bracket nowstands at its final width of 1 mA (for the purposes of example only) andthe midpoint of the bracket, 8.5 mA, is selected and reported asI_(thresh) for channel 2. It should again be appreciated that themidpoint of the bracket is selected as I_(thresh) for exemplary purposesand I_(thresh) could be reported as any value within the final bracket,such as for example, the upper end of the bracket or the lower end ofthe bracket.

Although the algorithm is discussed and shown to process channels innumerical order, it will be understood that the actual order in whichchannels are processed is immaterial. The channel processing order maybe biased to yield the highest or lowest threshold first (discussedbelow) or an arbitrary processing order may be used. Furthermore, itwill be understood that it is not necessary to complete the algorithmfor one channel before beginning to process the next channel. Channelsare still processed one at a time, however, the algorithm may cyclebetween one or more channels, processing as few as one stimulationcurrent for that channel before moving on to the next channel. By way ofexample only, the algorithm may stimulate at 1 mA while processing afirst channel for I_(thresh). Before stimulating at 2 mA (the nextstimulation current in the bracketing phase) the algorithm may cycle toany other channel and process it for the 1 mA stimulation current(omitting the stimulation if applicable). Any or all of the channels maybe processed this way before returning to the first channel to apply thenext stimulation. Likewise, the algorithm need not return to the firstchannel to stimulate at 2 mA, but instead may select a different channelto process first at the 2 mA level. In this manner, the algorithm mayadvance all channels essentially together and bias the order to find thelower threshold channels first or the higher threshold channels first.By way of example only, the algorithm may stimulate at one current leveland process each channel in turn at that level before advancing to thenext stimulation current level. The algorithm may continue in thispattern until the channel with the lowest I_(thresh) is bracketed. Thealgorithm may then process that channel exclusively until I_(thresh) isdetermined, and then return to processing the other channels onestimulation current level at a time until the channel with the nextlowest I_(thresh) is bracketed. This process may be repeated untilI_(thresh) is determined for each channel in order of lowest to highestI_(thresh). Should I_(thresh) for more than one channel fall within thesame bracket, the bracket may be bisected, processing each channelwithin that bracket in turn until it becomes clear which one has thelowest I_(thresh). If it becomes more advantageous to determine thehighest I_(thresh) first, the algorithm may continue in the bracketingstate until the bracket is found for every channel and then bisect eachchannel in descending order.

In another significant aspect of the present invention, to furtherreduce the number of stimulations required to repeatedly find I_(thresh)over the course of a procedure, the algorithm includes a confirmationstep. If I_(thresh) has been previously determined for a specificchannel, the algorithm may simply confirm that I_(thresh) has notchanged rather than beginning anew with the bracketing and bisectionmethods. FIG. 8 illustrates the overall sequence the algorithm followsto determine I_(thresh). The algorithm first determines whether it isconducting the initial threshold determination, for the channel orwhether there is a previous I_(thresh) determination (step 20). If it isnot the initial determination the algorithm confirms the previousdetermination (step 22), as described below. If the previous thresholdis confirmed, the algorithm reports that value as the present I_(thresh)(step 24). If it is the initial I_(thresh) determination, or if theprevious threshold cannot be confirmed, the algorithm enters thebracketing (step 26) and bisection (step 28) states to determineI_(thresh) and then reports the value (step 24).

FIG. 9 illustrates, by way of example only, a method employed by thealgorithm for confirming a previous threshold, I_(thresh). Theconfirmation step attempts to ascertain whether I_(thresh) has movedfrom its last known value. To do this, the algorithm applies twostimulation currents, one at or just above the threshold value and onejust below the threshold value. If the stimulation at or aboveI_(thresh) recruits and the stimulation just below I_(thresh) does notrecruit, then I_(thresh) is confirmed and the algorithm may report theinitial value again as I_(thresh) and proceed to process anotherchannel. If the stimulation just below I_(thresh) recruits, it may beconcluded that I_(thresh) has decreased and likewise, if the stimulationat or just above I_(thresh) fails to recruit, it may be assumed thatI_(thresh) has increased and therefore I_(thresh) can not be confirmed.

If I_(thresh) cannot be confirmed, the algorithm enters the bracketingstate. Rather than beginning the bracketing state from the minimumstimulation current, however, the bracketing state may begin from theprevious I_(thresh). The bracketing may advance up or down depending onwhether I_(thresh) has increased or decreased. By way of example only,if the previous value of I_(thresh) was 4 mA, the confirmation step maystimulate at 4 mA and 3.8 mA. If the stimulation at 4 mA fails to evokea significant response, it may be concluded that the I_(thresh) hasincreased and the algorithm will bracket upwards from 4 mA. When thealgorithm enters the bracketing state, the increment used in theconfirmation step (i.e. 0.2 mA in this example) is doubled. Thus thealgorithm stimulates at 4.4 mA. If the channel fails to recruit at thiscurrent level, the increment is doubled again to 0.8 mA, and thealgorithm stimulates at 5.2 mA. This process is repeated until themaximum stimulation current is reached or the channel recruits, at whichtime it may enter the bisection state.

If, during the confirmation step, the stimulation current just below thepreviously determined I_(thresh) recruits, it may be concluded thatI_(thresh) for that channel has decreased and the algorithm may bracketdown from that value (i.e. 3.8 mA in this example). Thus, in thisexample the algorithm would double the increment to 0.4 mA and stimulateat 3.4 mA. If the channel still recruits at this stimulation current,the increment is doubled again to 0.8 mA such that the algorithmstimulates at 2.6 mA. This process is repeated until the minimumstimulation current is reached, or the channel fails to recruit, atwhich time the algorithm may enter the bisection state. The confirmationstep may be performed for each channel, in turn, in any order. Againstimulations may be omitted and the algorithm may begin processing a newchannel before completing the algorithm for another channel, asdescribed above.

By way of example only, the algorithm of the present invention may beparticularly useful when employed to monitor nerve pathology inconjunction with the use of a nerve retractor, such as nerve retractor60 and 61 (shown in FIG. 10). A typical nerve retractor serves to pullor otherwise maintain a nerve outside the surgical corridor, therebyprotecting the nerve from inadvertent damage or contact by the “active”instrumentation used to perform the actual surgery. While generallyadvantageous, it has been observed that such retraction can cause nervefunction to become impaired or otherwise pathologic over time due to theretraction. Monitoring I_(thresh) during nerve retraction may be usefulto assess the degree to which retraction of a nerve or neural structureaffects the nerve function over time. One advantage of such monitoringis that the conduction of the nerve may be monitored during theprocedure to determine whether the neurophysiology and/or function ofthe nerve changes (for the better or worse) as a result of theparticular surgical procedure. For example, it may be observed that thenerve conduction decreases (indicated by an increase in I_(thresh) overtime) during the retraction, indicating that the nerve function has beennegatively affected. In contrast, the nerve conduction may increase(indicated by a decrease in I_(thresh) over time), indicating that thenerve function may have been restored or improved by the surgicalprocedure (such as during a successful decompression surgery, etc . . .). As mentioned, a change in I_(thresh) may occur on any channel;therefore it is advantageous to calculate the actual I_(thresh) for eachchannel, as opposed to determining a value for just the channel with thehighest or lowest I_(thresh). The algorithm of the present inventionaccomplishes this while substantially limiting the number ofstimulations required to do so. This may substantially reduce the timerequired to make an I_(thresh) determination which in turn may reducethe overall surgical time and risk to the patient.

By way of example only, the algorithm of the present invention may alsobe of particular use during Motor Evoked Potential (MEP) monitoring.When surgical procedures are performed in the proximity of the spinalcord, potential damage to the spinal cord is a paramount concern.Consequences of spinal cord damage may range from a slight loss ofsensation to complete paralysis of the extremities, depending on thelocation and extent of damage. MEP monitoring, which generally involvesmonitoring the transmission of an electrical signal along the spinalcord, may be employed to assess the spinal cord before, during, and/orafter surgery. Degradation or decreased conduction of an electricalsignal, indicated by an increase in I_(thresh), may indicate that thehealth of the spinal cord is compromised. Obtaining such informationquickly may allow the surgeon to initiate corrective measures before thedamage gets worse and/or becomes permanent. Similar to the nervepathology monitoring mentioned above, changes in I_(thresh) indicatingpotential damage to the spinal cord may occur on any monitored channel,thus it is advantageous to calculate the actual I_(thresh) for eachchannel, as opposed to determining just the channel with the highest orlowest I_(thresh). Employing the algorithm of the present inventionagain allows this to be done accurately and efficiently.

The algorithm of the present invention may be employed for use on any ofa number of neurophysiology monitoring systems, including but notlimited to that shown and described in commonly owned Int'l Patent App.No. PCT/US02/30617, entitled “System and Methods for Performing SurgicalProcedures and Assessments,” filed on Sep. 25, 2002; and Int'l PatentApp. No. PCT/US2006/003966, entitled “System and Methods for PerformingNeurophysiologic Assessments During Spine Surgery,” filed on Feb. 2,2006, both of which are hereby incorporated by reference as if set forthfully herein. FIG. 10 illustrates, by way of example only, amulti-channel neurophysiology monitoring system for employing thealgorithm of the present invention to quickly find stimulationthresholds for a multitude of channels. By way of example only, theneuromonitoring system 40 may be capable of carrying outneurophysiologic assessment functions including, but not necessarilylimited to, Twitch Test (neuromuscular pathway assessment), Screw Test(pedicle integrity testing), Detection (nerve proximity testing duringsurgical access), Nerve Retractor (nerve pathology monitoring), MEP(Motor Evoked Potential spinal cord monitoring), and SSEP (SomatosensoryEvoked Potential spinal cord monitoring). It is expressly noted that,although described herein largely in terms of use in spinal surgery, theneuromonitoring system 10 and related methods of the present inventionare suitable for use in any number of additional surgical procedureswhere neurological impairment is a concern.

The surgical system 40 includes a control unit 42, a patient module 44,an MEP stimulator 46, an EMG harness 48, including eight pairs of EMGelectrodes 50 and a return (anode) electrode 52 coupled to the patientmodule 44, at least one pair of stimulation electrodes 54 coupled to theMEP stimulator 46, and a host of surgical accessories 56 capable ofbeing coupled to the patient module 44 via one or more accessory cables58. The surgical accessories 56 may include, but are not necessarilylimited to, a neural pathology monitoring device such as nerve rootretractors 60 and 62. Additional surgical accessories may includestimulation accessories (such as a screw test probe 70 and dynamicstimulation clips 72, 74), surgical access components (such as a K-wire76, one or more dilating cannulae 78, 80, and a tissue retractionassembly 82).

FIG. 11 is a block diagram of the surgical system 40, the operation ofwhich will be explained in conjunction with FIG. 10. The control unit 42includes a touch screen display 64 and a base 66, which collectivelycontain the essential processing capabilities for controlling thesurgical system 40. The touch screen display 64 is preferably equippedwith a graphical user interface (GUI) capable of graphicallycommunicating information to the user and receiving instructions fromthe user. The base 66 contains computer hardware and software thatcommands the stimulation sources (e.g. MEP stimulator 46 and patientmodule 44) receives digital and/or analog signals and other informationfrom the patient module 44, processes the EMG responses, and displaysthe processed data to the operator via the display 64. The primaryfunctions of the software within the control unit 42 include receivinguser commands via the touch screen display 64, activating stimulation ina requested mode (e.g. Screw Test (Basic, Difference, Dynamic),Detection, Nerve Retractor, MEP, SSEP, Twitch Test), processing signaldata according to defined algorithms (described below), displayingreceived parameters and processed data, and monitoring system status.

The patient module 44 is connected via a data cable 67 to the controlunit 42, and contains the electrical connections to electrodes, signalconditioning circuitry, stimulator drive and steering circuitry, and adigital communications interface to the control unit 42. In use, thecontrol unit 42 is situated outside but close to the surgical field(such as on a cart adjacent the operating table) such that the display64 is directed towards the surgeon for easy visualization. The patientmodule 44 may be located near the patient's legs or may be affixed tothe end of the operating table at mid-leg level using a bedrail clamp.The position selected should be such that all EMG electrodes can reachtheir farthest desired location without tension during the surgicalprocedure. The information displayed to the user on the display 62 mayinclude, but is not necessarily limited to, alpha-numeric and/orgraphical information regarding MEP, nerve pathology, myotome/EMGlevels, stimulation levels, the function selected, and the instrument inuse.

In a preferred embodiment, EMG response monitoring for the system 40 isaccomplished via 8 pairs of EMG electrodes 50 placed on the skin overthe muscle groups to be monitored, a common electrode 51 providing aground reference to pre-amplifiers in the patient module 44, and ananode electrode 52 providing a return path for the stimulation current.The EMG responses provide a quantitative measure of the nervedepolarization caused by the electrical stimulus. It should beappreciated that any of a variety of known electrodes can be employedwith system 40, including but not limited to surface pad electrodes andneedle electrodes. An exemplary EMG electrode is the dual surfaceelectrode shown and described in detail in the commonly owned andco-pending U.S. patent application Ser. No. 11,048,404, entitled“Improved Electrode System and Related Methods,” filed on Jan. 31, 2005,which is expressly incorporated by reference into this disclosure as ifset forth in its entirety herein.

The arrangement of EMG electrodes depends on a multitude of factors,including for example, the spinal cord level, neural tissue at risk, anduser preference, among others. In one embodiment (set forth by way ofexample only), the preferred EMG configuration is described for Lumbarsurgery in Table 1, Thoracolumbar surgery in Table 2, and Cervicalsurgery in Table 3 below:

TABLE 1 Lumbar Color Channel Myotome Nerve Spinal Level Red Right 1Right Vastus Medialis Femoral L2, L3, L4 Orange Right 2 Right TibialisAnterior Common L4, L5 Peroneal Yellow Right 3 Right Biceps FemorisSciatic L5, S1, S2 Green Right 4 Right Medial Gastroc. Post Tibial S1,S2 Blue Left 1 Left Vastus Medialis Femoral L2, L3, L4 Violet Left 2Left Tibialis Anterior Common L4, L5 Peroneal Gray Left 3 Left BicepsFemoris Sciatic L5, S1, S2 White Left 4 Left Medial Gastroc. Post TibialS1, S2

TABLE 2 Thoracolumbar Color Channel Myotome Nerve Spinal Level Red Right1 Right Abductor Pollicis Median C6, C7, C8, T1 Brevis Orange Right 2Right Vastus Medialis Femoral L2, L3, L4 Yellow Right 3 Right TibialisAnterior Common L4, L5 Peroneal Green Right 4 Right Abductor HallucisTibial L4, L5, S1 Blue Left 1 Left Abductor Pollicis Median C6, C7, C8,T1 Brevis Violet Left 2 Left Vastus Medialis Femoral L2, L3, L4 GrayLeft 3 Left Tibialis Anterior Common L4, L5 Peroneal White Left 4 LeftAbductor Hallucis Tibial L4, L5, S1

TABLE 3 Cervical Color Channel Myotome Nerve Spinal Level Red Right 1Right Deltoid Axilliary C5, C6 Orange Right 2 Right Flexor Carpi MedianC6, C7, C8 Radialis Yellow Right 3 Right Abductor Pollicis Median C6,C7, C8, T1 Brevis Green Right 4 Right Abductor Hallucis Tibial L4, L5,S1 Blue Left 1 Left Deltoid Axillary C5, C6 Violet Left 2 Left FlexorCarpi Median C6, C7, C8 Radialis Gray Left 3 Left Abductor PollicisMedian C6, C7, C8, T1 Brevis White Left 4 Left Abductor Hallucis TibialL4, L5, S1

The surgical system 40 employs the algorithm described above toautomatically control the delivery of stimulation signals upon testinitiation. While it may be used with any of a number of the operablefunctions of system 40, the multi-channel aspect of the huntingalgorithm will be described by way of example only during NerveRetractor and MEP modes, which will be described in greater detailbelow. Various additional functions of the system 40 have beenpreviously discussed in detail elsewhere and such discussion is notincluded herein. Details of the Twitch Test, Screw Test (Basic,Difference, Dynamic), Detection, and SSEP modes may be found in thefollowing commonly owned patent applications, each of which is expresslyincorporated by reference as if set forth herein in their entireties:Int'l Patent App. No. PCT/US2005/036089, entitled “System and Methodsfor Assessing the Neuromuscular Pathway Prior to Nerve Testing,” filedOct. 7, 2005; Int'l Patent App. No. PCT/US02/35047 entitled “System andMethods for Performing Percutaneous Pedicle Integrity Assessments,”filed on Oct. 30, 2002; Int'l Patent App. No. PCT/US2004/025550,entitled “System and Methods for Performing Dynamic Pedicle IntegrityAssessments,” filed on Aug. 5, 2004; Int'l Patent App. NoPCT/US02/22247, entitled “System and Methods for Determining NerveProximity, Direction, and Pathology During Surgery,” filed on Jul. 11,2002; the entire contents of each are hereby incorporated by referenceas if set forth fully herein.

The surgical system 40 accomplishes neural pathology monitoring (viaNerve Retractor Mode, by way of example only) by electricallystimulating a nerve root according to the hunting algorithm, via one ormore stimulation electrodes at the distal end of the nerve rootretractor 60 or 61 and monitoring each channel for corresponding evokedmuscle responses. Threshold hunting continues according to the algorithmuntil I_(thresh) is determined for each channel in range. A pathologyassessment is made by determining a baseline stimulation threshold withdirect contact between the nerve retractor 60 or 61 and the nerve, priorto retraction. Subsequent stimulation thresholds are determined duringretraction and they are compared to the baseline threshold. An increasein I_(thresh) over time is an indication that the nerve function isdeteriorating and retraction should be reduced or stopped altogether toprevent permanent damage. A decrease in I_(thresh) over time may be anindication that nerve function has been at least partially restored.

I_(thresh) results determined by the algorithm may be displayed to thesurgeon on the exemplary screen display of FIG. 12 (to be displayed ondisplay 64 of FIG. 10). Preferably, baseline, directly previous, andcurrent I_(thresh) results are shown for each channel. The display ofI_(thresh) values may be accompanied by a color code making use of thecolors Red, Yellow, and Green. The color Red may be displayed when thedifference between the baseline and actual value is within apredetermined “unsafe” level. The color Green may be displayed when thedifference between the baseline I_(thresh) and current I_(thresh) iswithin a predetermined “safe” level. Yellow may be displayed whendifference between the baseline thresh and current I_(thresh) fallsbetween predetermined unsafe and safe levels.

The nerve root retractor 60 may be dimensioned in any number ofdifferent fashions, such as retractors 60 and 61 illustrated in FIG. 10,including having a generally curved distal region (shown as a side viewin FIG. 10 to illustrate the concave region where the nerve wilt bepositioned while retracted), and of sufficient dimension (width and/orlength) and rigidity to maintain the retracted nerve in a desiredposition during surgery. The nerve root retractors 60, 61 may also beequipped with a handle 68 having one or more buttons for selectivelyinitiating the algorithm and applying the electrical stimulation to thestimulation electrode(s) at the distal end of the nerve root retractor60, 61. In one embodiment, the nerve root retractor 60, 61 is disposableand the handle 68 is reusable and autoclavable.

The surgical system 40 may perform MEP by electrically stimulating themotor cortex of the brain with electrical stimulation signals whichcreates an action potential that travels along the spinal cord and intothe descending nerves, evoking activity from muscles innervated by thenerves. EMG responses of the muscles are recorded by the system 40 andanalyzed in relation to the stimulation signal. Stimulation and analysisare preferably executed according to the multi-channel hunting algorithmdescribed above.

MEP stimulation signals are generated in the MEP stimulator 21 anddelivered to the motor cortex via a pair of stimulation electrodes 54connected to the MEP stimulator 21 and placed on opposite sides of thecranium. Each MEP signal is preferably delivered as a group or train ofmultiple pulses, such as that illustrated in FIG. 3. Stimulation signalsare delivered at a constant current but MEP stimulator 46 is capable ofdelivering stimulation signals over a large range of currents in orderto execute the hunting algorithm. By way of example only, MEP stimulator46 may deliver a first stimulation signal at a constant current of 100mA, a second stimulation signal at constant current of 200 mA, a thirdstimulation signal of 400 mA, and a fourth stimulation signal of 800 mA.Preferably, stimulation signals may be delivered at a current rangingfrom 0 mA to 100 mA. It should be understood of course that the huntingalgorithm employed by the system 40 need not be limited to any range.MEP stimulator 46 may deliver either a positive pulse or a negativepulse. Additionally, MEP stimulator 46 may have more than onestimulation channel, thus, additional pairs of stimulation electrodes 54may be arranged on the skull. This is advantageous in that theeffectiveness of a stimulation signal originating from one position onthe skull may vary between different recording sites.

MEP stimulator 46 is communicatively linked to the control unit 42 whichcommands the stimulator 46 to deliver electrical signals according topredetermined parameters (such as current level, among others) at theproper time. MEP stimulator 46 may be communicatively linked to thecontrol unit 40 via any suitable connection such as a data cable orwireless technology, etc . . . The MEP stimulator 46 may be positionedoutside the sterile area but should be located such that the stimulationelectrodes 54, attached to the stimulator 46, may be positioned on thepatient's head without tension. By way of example, MEP stimulator 46 maybe placed on the surgical table adjacent to the patient's head.Optionally, the MEP stimulator 46 may be fashioned with a mount or hook(not shown) and hung from an IV pole near the patient's head.

The multi-channel threshold hunting algorithm described above isutilized to determine a baseline I_(thresh) for each channel, preferablyprior to or in the early stages of a surgical procedure. It should beappreciated, however, that a new baseline I_(thresh) may be determinedat any time during the procedure at the option of the surgeon or otherqualified operator. Having determined a baseline I_(thresh) for eachchannel, subsequent monitoring may be performed as desired throughoutthe procedure and recovery period to obtain updated I_(thresh) valuesfor each channel. Each new determination of I_(thresh) is compared bythe surgical system 40 to the baseline I_(thresh) for the appropriatechannel. The difference (ΔI_(thresh)) between the baseline I_(thresh)and the new I_(thresh) is calculated by the system 40 and theΔI_(thresh) value is compared to predetermined “safe” and “unsafe”values. If ΔI_(thresh) is greater than the predetermined safe level, theuser is alerted to a potential complication and action may be taken toavoid or mitigate the problem. The speed with which the multi-channelMEP threshold hunting algorithm is able to determine I_(thresh) acrossall channels, and the simplicity with which the data communicated to theuser may be interpreted, allows the user to increase the frequency ofMEP monitoring conducted during a procedure without a concurrentincrease in overall surgery time. This provides significant benefit tothe patient by reducing the time intervals in between MEP monitoringepisodes during which an injury to the spinal cord may go undetected.

The display of I_(thresh), shown by way of example only in the exemplaryMEP screen display of FIG. 13 (to be displayed on display 64 of FIG.10), may be accompanied by a color code so that the operator may quicklyeasily comprehend the situation and avoid neurological impairment to thepatient (e.g. red for “danger,” yellow for “caution” and green for“safe”). The color Red may be displayed when the difference between thebaseline and actual value, ΔI_(thresh), is within a predetermined“unsafe” level. The color Green may be displayed when the ΔI_(thresh) iswithin a predetermined “safe” level. Yellow may be displayed when theΔI_(thresh) value falls between predetermined unsafe and safe levels.

It will be readily appreciated that various modifications may beundertaken, or certain steps or algorithms omitted or substituted,without departing from the scope of the present invention. By way ofexample only, although the multi-channel hunting algorithm is discussedherein in terms of finding I_(thresh) (the lowest stimulation currentthat evokes a significant EMG response), it is contemplated thatalternative stimulation thresholds may be determined by the huntingalgorithm. By way of example only, the hunting algorithm may be employedto determine a stimulation voltage threshold, Vstim_(thresh). This isthe lowest stimulation voltage (as opposed to the lowest stimulationcurrent) necessary to evoke a significant EMG response,

V_(thresh). The bracketing and bisection states are conducted, omittingstimulations and conducting confirmation step when applicable, asdescribed above., with brackets based on voltage being substituted forthe current based brackets previously described. By way of furtherexample, although use of the multi-channel hunting algorithm wasdescribed with reference to a nerve retractor and MEP monitoring, itwill be appreciated that the algorithm may be employed for a variety orneurophysiology functions including, but not necessarily limited to,pedicle integrity testing, nerve proximity monitoring, and nervedirection monitoring.

Moreover, although use of the algorithm was illustrated with referenceto the surgical system 40, it will be appreciated as within the scope ofthe invention to use the multi-channel hunting algorithm as describedherein with any number of different neurophysiology based testingsystems.

While this invention has been described in terms of a best mode forachieving this invention's objectives, it will be appreciated by thoseskilled in the art that variations may be accomplished in view of theseteachings without deviating from the spirit or scope of the presentinvention. For example, the present invention may be implemented usingany combination of computer programming software, firmware or hardware.As a preparatory step to practicing the invention or constructing anapparatus according to the invention, the computer programming code(whether software or firmware) according to the invention will typicallybe stored in one or more machine readable storage mediums such as fixed(hard) drives, diskettes, optical disks, magnetic tape, semiconductormemories such as ROMs, PROMs, etc., thereby making an article ofmanufacture in accordance with the invention. The article of manufacturecontaining the computer programming code is used by either executing thecode directly from the storage device, by copying the code from thestorage device into another storage device such as a hard disk, RAM,etc. or by transmitting the code on a network for remote execution. Ascan be envisioned by one of skill in the art, many differentcombinations of the above may be used and accordingly the presentinvention is not limited by the specified scope.

1. A method for performing neurophysiologic assessments, comprising:delivering a plurality of electrical stimulation signals near tissue;detecting neuromuscular responses evoked by said signals; and omittingstimulation signals when said neuromuscular response is predictable. 2.The method of claim 1, wherein said plurality of stimulation signals areeach defined by a selected current level.
 3. The method of claim 2,wherein at least one of pedicle integrity, nerve proximity, nervepathology, and spinal cord health may be determined based upon anidentified relationship between said current levels and saidneuromuscular responses.
 4. The method of claim 3, wherein saididentified relationship is the threshold current level required to evokea neuromuscular response of a predetermined magnitude.
 5. The method ofclaim 4, wherein said neuromuscular response is detected by an EMGsensor and said magnitude is measured as a peak-to-peak voltage.
 6. Themethod of claim 5, wherein said predetermined magnitude is apeak-to-peak voltage from within the range of 20 uV to 100 uV.
 7. Themethod of claim 5, wherein at least two sensors are deployed to monitorthe neuromuscular response of different myotomes, said at least twosensors corresponding to different monitoring channels, and wherein athreshold current level is determined for each channel.
 8. The method ofclaim 7, wherein a threshold current level is found for between 2 to 8channels.
 9. The method of claim 7, wherein determining the thresholdcurrent for each channel comprises establishing for each channel abracket within which the threshold current must lie.
 10. The method ofclaim 7, wherein determining the threshold current for each channelcomprises bisecting to a predetermined range a bracket established oneach channel.
 11. The method of claim 7, wherein an algorithm isexecuted to determine the threshold current for each channel.
 12. Themethod of claim 11, wherein said algorithm is biased to find thethreshold current on the channel with the lowest threshold first andfind the threshold current on the channel with the highest thresholdfirst.
 13. The method of claim 11, wherein said algorithm operates byassigning a channel specific status to each selected stimulationcurrent, said status being one of a first status and a second status,said first status applying to a current level which evokes aneuromuscular response one of equal to and greater than saidpredetermined magnitude and said second status applying to a currentlevel which evokes a neuromuscular response less than said predeterminedmagnitude.
 14. The method of claim 13, wherein said algorithm determinessaid status of a selected stimulation current by at least one ofstimulating at said selected current and measuring the magnitude of theneuromuscular response, and inferring the status based on previouslycaptured data.
 15. The method of claim 14, wherein said previouslycaptured data includes the neuromuscular response measured on onechannel in relation to a stimulation current delivered to determine thethreshold current on another channel.
 16. The method of claim 13,wherein said first status is assigned without directing a stimulation atsaid selected current if said selected current is one of equal to andgreater than a current level previously assigned said first status onthe applicable channel.
 17. The method of claim 13, wherein said secondstatus is assigned without directing a stimulation signal at saidselected current if said selected current is less than a current levelpreviously assigned said second status on the applicable channel. 18.The method of claim 13, wherein said algorithm is based on successiveapproximation.
 19. The method of claim 13, wherein said algorithmcomprises a first step of establishing a bracket within which thethreshold current must lie and a second step of bisecting the bracket toa predetermined width.
 20. The method of claim 19, wherein saidbracketing step is carried out for each channel before the algorithmtransitions to said bisection step.
 21. The method of claim 19, whereinsaid first step of establishing a bracket and said second step ofbisecting the bracket are completed on one channel before beginning thesteps again on another channel.
 22. The method of claim 19, wherein saidfirst bracket is determined by assigning on each channel one of saidfirst status and said second status to a series of successively doublingstimulation currents, wherein an upper boundary of said first bracketcomprises the first stimulation current assigned said first status and alower boundary of said first bracket comprises the last stimulationcurrent assigned said second status.
 23. The method claim 22, whereinsaid first bracket is bisected on each channel by assigning one of saidfirst status and said second status to a stimulation current level atthe midpoint of said first bracket, said midpoint forming a secondbracket with one of said upper boundary and said lower boundary of saidfirst bracket, said midpoint forming a lower boundary of said secondbracket when said midpoint is assigned said first status and saidmidpoint forming an upper boundary of said second bracket when saidmidpoint is assigned said second status.
 24. The method of claim 4,wherein said threshold current level is determined for each of at leasttwo channels.
 25. The method of claim 24, wherein the current of saidstimulation signals is automatically adjusted in a first sequence toestablish a bracket which contains the stimulation threshold on a firstchannel.
 26. The method of claim 25, wherein the current of saidstimulation signals is automatically adjusted in a second sequence toestablish a bracket which contains the stimulation threshold on a secondchannel, wherein at least one of said stimulation currents in saidsecond sequence equals one of said stimulation currents in said firstsequence, and said stimulation current is omitted from said secondsequence.
 27. The method of claim 4, wherein delivery of saidstimulation signals is directed from a control unit, said control unitcommunicating with a stimulation source and a sensor configured todetect the neuromuscular responses.
 28. The method of claim 27,comprising the additional step of displaying on a display linked to saidcontrol unit at least one of alpha-numeric, graphic, and color basedindica to communicate the stimulation threshold results.
 29. The methodof claim 28, wherein said display includes a graphical user interfaceand comprising the further step of imputing user instructions includingone or more of starting stimulation, stopping stimulation, selecting afunction, and adjusting system parameters.
 30. The method of claim 27,wherein the stimulation source is an MEP stimulator and wherein thestimulation signals are delivered to the motor cortex.
 31. The method ofclaim 30, comprising the further step of determining the thresholdcurrent at least twice during a surgical procedure and comparing thelater threshold current to the earlier threshold current to indicate thehealth of the spinal cord.
 32. The method of claim 27, comprising thefurther step of retracting a nerve out of a surgical corridor using anerve retractor electrically coupled to said stimulation source.
 33. Themethod of claim 32, comprising the further step of determining thethreshold current at least twice while the nerve is retracted andcomparing the later threshold current to the earlier threshold currentto indicate the pathology of the nerve.
 34. A software applicationoperated on a neurophysiology monitoring system, said softwareapplication configured to (a) command delivery of a plurality ofstimulation signals at selected current amplitudes, (b) receive andprocess neuromuscular response data evoked by stimulation at saidcurrents, (c) establish for each selected current amplitude one of afirst status and a second status based on the magnitude of thecorresponding neuromuscular response, and (d) omit delivery of astimulation signal of at least one selected current when the status ofsaid at least one current is predictable.
 35. The software applicationof claim 34, wherein at least one of pedicle integrity, nerve proximity,nerve pathology, and spinal cord health is assessed based upon anidentified relationship between said current amplitudes and saidneuromuscular responses.
 36. The software application of claim 35,wherein said identified relationship is the threshold amplitudenecessary to evoke a neuromuscular response of a predeterminedmagnitude.
 37. The software application of claim 36, wherein saidsoftware application receives neuromuscular response data over at leasttwo channels corresponding to an equal number of sensors deployed tomonitor neuromuscular responses of different myotomes.
 38. The softwareapplication of claim 37, wherein said threshold current is determinedfor each of the at least two channels.
 39. The software application ofclaim 38, wherein there are 8 channels relaying neuromuscular responsedata said threshold current is determined for all 8 channels.
 40. Thesoftware application of claim 36, wherein said first status applies to acurrent level which evokes a neuromuscular response one of equal to andgreater than said predetermined magnitude and said second status appliesto a current level which evokes a neuromuscular response less than saidpredetermined magnitude.
 41. The software application of claim 34,wherein said software application access a memory on saidneurophysiology monitoring system and retrieves data corresponding toneuromuscular responses associated with previous stimulations.
 42. Thesoftware application of claim 41, wherein the data retrieved is used bythe software application to infer one of said first status and saidsecond status to a given stimulation current and omit delivery of thatstimulation current.
 43. The software application of claim 38, whereinsaid software application executes an algorithm to determine saidthreshold current for each of the at least two channels.
 44. Thesoftware application of claim 43, wherein said algorithm is based onsuccessive approximation.
 45. The software application of claim 43,wherein said algorithm comprises a first step of establishing a bracketwithin which the threshold current must lie and a second step ofbisecting the bracket to a predetermined width.
 46. The softwareapplication of claim 43, wherein said bracketing step is carried out foreach channel before the algorithm transitions to said bisection step.47. The software application of claim 43, wherein said first step ofestablishing a bracket and said second step of bisecting the bracket arecompleted on one channel before beginning the steps again on anotherchannel.
 48. The software application of claim 41, wherein said softwareapplication determines said status of a selected stimulation current byat least one of directing stimulation at said selected current andmeasuring the magnitude of the neuromuscular response, and inferring thestatus based on the neuromuscular response data from previousstimulations.
 49. The software application of claim 48, wherein saidprevious neuromuscular response data includes neuromuscular responsesmeasured on one channel in relation to a stimulation current deliveredto determine the threshold current on another channel.
 50. The softwareapplication claim 34, wherein said first status is assigned withoutdirecting a stimulation at said selected current if said selectedcurrent is one of equal to and greater than a current level previouslyassigned said first status on the applicable channel.
 51. The softwareapplication claim 34, wherein said second status is assigned withoutdirecting a stimulation signal at said selected current if said selectedcurrent is less than a current level previously assigned said secondstatus on the applicable channel.
 52. The software application of claim36, wherein said software application is further configured to directthe display of at least one of alpha-numeric, graphic, and color basedindica to communicate the stimulation threshold results.
 53. Thesoftware application of claim 36, wherein said software application isfurther configured to receive user instructions from a graphical userinterface including one or more of starting stimulation, stoppingstimulation, selecting a function, and adjusting system parameter
 54. Analgorithm for directing the delivery of stimulation signals to determinethe stimulation threshold of neural tissue relative to at least two EMGmonitoring channels, comprising the steps of: (a) determining a firststimulation threshold on a first channel; (b) establishing for a secondchannel a bracket within which a second stimulation threshold must lie;(c) bisecting the bracket to a predetermined range and selecting anyvalue within the predetermined range as the second stimulationthreshold; and (d) omitting the delivery of at least one stimulationsignal during at least one of step (b) and step (c) when the EMGresponse to a selected stimulation current is predictable.
 55. Thealgorithm of claim 54, wherein the step (b) of establishing a bracketfor the second channel comprises the further steps of selecting a seriesof stimulation currents and determining an EMG result for each current.56. The algorithm of claim 55, wherein the step of determining an EMGresult is accomplished via one of stimulating at the selected currentwhile monitoring the EMG response, and examining previously obtained EMGresponses to infer the likely result.
 57. The algorithm of claim 56,wherein the series of stimulation currents begins at a predeterminedminimum current and exponentially doubles thereafter until an EMG resultof a predetermined magnitude is evoked, the first simulation currentdetermined to evoke the predetermined magnitude response forms an upperboundary of the bracket and the last stimulation current determined tonot evoke the predetermined magnitude response forms a lower boundary ofthe bracket.
 58. The algorithm of claim 57, wherein the step (c) ofbisecting the bracket formed in step (b) comprises the further step ofdetermining an EMG result for a stimulation current at the midpoint ofthe bracket and shrinking the bracket to one of an upper half and alower half based upon the EMG result at the midpoint and then repeatingthe step until a bracket of a predetermined range is formed, the secondstimulation threshold being selected as any value within the finalbracket.
 59. The algorithm of claim 58, wherein the step of determiningan EMG result for the midpoint current is accomplished via one ofstimulating at the midpoint current while monitoring the EMG response,and examining previously obtained EMG responses to infer the likelyresult.